Query Construction Research Articles

Data-driven construction of SPARQL queries by approximate question graph alignment in question answering over knowledge graphs

As increasingly more semantic real-world data is stored in knowledge graphs, providing intuitive and effective query methods for end-users is a fundamental and challenging task. Since there is a gap between the plain natural language question (NLQ) and structured data, most RDF question/answering (Q/A) systems construct SPARQL queries from NLQs and obtain precise answers from knowledge graphs. A major challenge is how to disambiguate the mapping of phrases and relations in a question to the dataset items, especially in complex questions. In this paper, we propose a novel data-driven graph similarity framework for RDF Q/A to extract the query graph patterns directly from the knowledge graph instead of constructing them with semantically mapped items. An uncertain question graph is presented to model the interpretations of an NLQ, based on which our problem is reduced to a graph alignment problem. In formulating the alignment, both the lexical and structural similarity of graphs are considered, hence, the target RDF subgraph is used as a query graph pattern to construct the final query. We create a pruned entity graph dynamically based on the complexity of an input question to reduce the search space on the knowledge graph. Moreover, to reduce the calculating cost of the graph similarity, we compute the similarity scores only for same-distance graph elements and equip the process with an edge association-aware surface form extraction method. Empirical studies over real datasets indicate that our proposed approach is flexible and effective as it outperforms state-of-the-art methods significantly.

Automatic Detection of Objects in 3D Point Clouds Based on Exclusively Semantic Guided Processes

In the domain of computer vision, object recognition aims at detecting and classifying objects in data sets. Model-driven approaches are typically constrained through their focus on either a specific type of data, a context (indoor, outdoor) or a set of objects. Machine learning-based approaches are more flexible but also constrained as they need annotated data sets to train the learning process. That leads to problems when this data is not available through the specialty of the application field, like archaeology, for example. In order to overcome such constraints, we present a fully semantic-guided approach. The role of semantics is to express all relevant knowledge of the representation of the objects inside the data sets and of the algorithms which address this representation. In addition, the approach contains a learning stage since it adapts the processing according to the diversity of the objects and data characteristics. The semantic is expressed via an ontological model and uses standard web technology like SPARQL queries, providing great flexibility. The ontological model describes the object, the data and the algorithms. It allows the selection and execution of algorithms adapted to the data and objects dynamically. Similarly, processing results are dynamically classified and allow for enriching the ontological model using SPARQL construct queries. The semantic formulated through SPARQL also acts as a bridge between the knowledge contained within the ontological model and the processing branch, which executes algorithms. It provides the capability to adapt the sequence of algorithms to an individual state of the processing chain and makes the solution robust and flexible. The comparison of this approach with others on the same use case shows the efficiency and improvement this approach brings.

Structured query construction via knowledge graph embedding

In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query. Existing query construction methods rely on question understanding and conventional graph-based algorithms which lead to inefficient and degraded performances facing complex natural language questions over knowledge graphs with large scales. In this paper, we focus on this problem and propose a novel framework standing on recent knowledge graph embedding techniques. Our framework first encodes the underlying knowledge graph into a low-dimensional embedding space by leveraging generalized local knowledge graphs. Given a natural language question, the learned embedding representations of the knowledge graph are utilized to compute the query structure and assemble vertices/edges into the target query. Extensive experiments were conducted on the benchmark dataset, and the results demonstrate that our framework outperforms state-of-the-art baseline models regarding effectiveness and efficiency.

On the design of linked datasets mapping networks of collaboration in the genomic sequencing of Saccharomyces cerevisiae, Homo sapiens, and Sus scrofa.

This paper describes a unique two-step methodology used to construct six linked bibliometric datasets covering the sequencing of Saccharomyces cerevisiae, Homo sapiens, and S us scrofa genomes. First, we retrieved all sequence submission data from the European Nucleotide Archive (ENA), including accession numbers associated with each species. Second, we used these accession numbers to construct queries to retrieve peer-reviewed scientific publications that first linked to these sequence lengths in the scientific literature. For each species, this resulted in two associated datasets: 1) A .csv file documenting the PMID of each article describing new sequences, all paper authors, all institutional affiliations of each author, countries of institution, year of first submission to the ENA, and the year of article publication, and 2) A .csv file documenting all institutions submitting to the ENA, number of nucleotides sequenced, number of submissions per institution in a given year, and years of submission to the database. In several upcoming publications, we utilise these datasets to understand how institutional collaboration shaped sequencing efforts, and to systematically identify important institutions and changes in network structures over time. This paper, therefore, should aid researchers who would like to use these data for future analyses by making the methodology that underpins it transparent. Further, by detailing our methodology, researchers may be able to utilise our approach to construct similar datasets in the future.

Semi-automatic inductive construction of reference process models that represent best practices in public administrations: A method

Business process management often uses reference models to improve processes or as starting point when creating individual process models. The current academic literature offers primarily deductive methods with which to develop these reference models, although some methods develop reference models inductively from a set of individual process models, focusing on deriving and representing common practices. However, there is no inductive method with which to detect best practices and represent them in a reference model. This paper addresses this research gap by proposing a method by which to develop reference process models that represent best practices in public administrations semi-automatically and inductively. The method uses a merged model that retains the structure of the source models while detecting their common parts. It identifies best practices using query constructs and ranking criteria to group the source models’ elements and to evaluate these groups. We provide a conceptualization of the method and demonstrate its functionality using an artificial example. We describe our implementation of the method in a software prototype and report on its evaluation in a workshop with domain and method experts who applied the method to real-world process models.

“Computer what's your favourite colour?” children's information‐seeking strategies in the classroom

ABSTRACTThis research investigates children's information seeking in primary and intermediate school classrooms as seen by their teachers. We report the results of a series of semi‐structured interviews with teachers of pre‐teen children. The teachers discussed specific issues that children encounter during information search, including query construction and information triage difficulties. Further issues were identified during the interviews regarding motivation, frustration and poor strategies for overcoming barriers. We compare the results of our teacher interviews reported here with the results of an interview study with children. This paper concludes that even with significant advancements in technology, teachers and children still require further assistance in order to succeed in their inquiry practices.

Some features of query construction in an expert system according to the peculiarities of knowledge structuring (using the domain of nanotechnology as an example)

Статья посвящена вопросу интеллектуализации информационного поиска и ускорения процесса извлечения научных знаний из специализированного программного обеспечения — экспертной системы, научные знания в которой структурированы в виде терминологических сетей. Задачей исследования явилось представление способа извлечения необходимой информации из экспертной системы путем ввода конкретного запроса и перечисление возможных запросов для системы. Базовой терминологической сетью в настоящей работе выступает фрагмент реконструируемой авторами обобщенной терминологической сети, актуальной для терминов категории «Инструмент (искусственный объект)». К рассматриваемой категории относятся термины квантовая точка, нанопленка, нановолокно, нанопроволока, нанопластина, контексты употребления которых послужили источниками примеров. Основными методами исследования в процессе работы стали контекстный анализ и метод интерпретации полученных данных, которые на начальном этапе способствовали разработке семантических отношений, связывающих термины категории «Инструмент области нанотехнологии» со смежными терминами других категорий. Выделенные семантические отношения между терминами позволили определить характер их системного взаимодействия и далее выстроить некоторые прототипичные модели взаимодействия понятий. Сопоставив набор полученных семантических отношений и прототипичных моделей для вышеперечисленных терминов категории «Инструмент области нанотехнологии», авторам удалось реконструировать фрагмент обобщенной терминологической сети. Данные результаты послужили основанием для формулировки некоторых типовых вопросов, которые следует использовать в качестве поисковых запросов для извлечения научных знаний из экспертной системы. Показано, что сформированные запросы универсальны и позволяют получить необходимую информацию в случае наличия соответствующих семантических отношений в терминологической сети запрашиваемого термина.

SQL Query Construction from Ontology Concept Descriptions

Based on the usage of previously proposed database concepts as mapping point to a database in a domain ontology, the present paper describes the process of constructing SQL queries from them. The proposed database concepts allow for the mapping of domain concept to the source of data from a database. The paper describes the process of traversing the class hierarchy in an ontology for gathering these database concepts and constructing the SQL query. The purpose of the constructed SQL query is to obtain data from a database to populate the ontology with instances related to a selected ontology concept. The described process begins with the selection of one ontology concept, obtaining all directly related concepts, filtering and collecting database concepts, and finally constructing the SQL query.

OptiqueVQS: A visual query system over ontologies for industry

An important application of semantic technologies in industry has been the formalisation of information models using OWL 2 ontologies and the use of RDF for storing and exchanging application data. Moreover, legacy data can be virtualised as RDF using ontologies following the ontology-based data access (OBDA) approach. In all these applications, it is important to provide domain experts with query formulation tools for expressing their information needs in terms of queries over ontologies. In this work, we present such a tool, OptiqueVQS, which is designed based on our experience with OBDA applications in Statoil and Siemens and on best HCI practices for interdisciplinary engineering environments. OptiqueVQS implements a number of unique techniques distinguishing it from analogous query formulation systems. In particular, it exploits ontology projection techniques to enable graph-based navigation over an ontology during query construction. Secondly, while OptiqueVQS is primarily ontology driven, it exploits sampled data to enhance selection of data values for some data attributes. Finally, OptiqueVQS is built on well-grounded requirements, design rationale, and quality attributes. We evaluated OptiqueVQS with both domain experts and casual users and qualitatively compared our system against prominent visual systems for ontology-driven query formulation and exploration of semantic data. OptiqueVQS is available online and can be downloaded together with an example OBDA scenario.

Deep Learning and Semantic Concept Spaceare Used in Query Expansion

In the practice of information retrieval, there are some problems such as the lack of accurate expression of user query requests, the mismatch between document and query and query optimization. Focusing on these problems, we propose the query expansion method based on conceptual semantic space with deep learning, this hybrid query expansion technique include deep learning and pseudocorrelation feedback, use the deep learning and semantic network WordNet to construct query concept tree in the level of concept semantic space, the pseudo-correlation feedback documents are processed by observation window, compute the co-occurrence weight of the words by using the average mutual information and get the final extended words set. The results of experiment show that the expansion algorithm based on conceptual semantic space with deep learning has better performance than the traditional pseudo-correlation feedback algorithm on query expansion.

Learning From Unannotated QA Pairs to Analogically Disambiguate and Answer Questions

Creating systems that can learn to answer natural language questions has been a longstanding challenge for artificial intelligence. Most prior approaches focused on producing a specialized language system for a particular domain and dataset, and they required training on a large corpus manually annotated with logical forms. This paper introduces an analogy-based approach that instead adapts an existing general purpose semantic parser to answer questions in a novel domain by jointly learning disambiguation heuristics and query construction templates from purely textual question-answer pairs. Our technique uses possible semantic interpretations of the natural language questions and answers to constrain a query-generation procedure, producing cases during training that are subsequently reused via analogical retrieval and composed to answer test questions. Bootstrapping an existing semantic parser in this way significantly reduces the number of training examples needed to accurately answer questions. We demonstrate the efficacy of our technique using the Geoquery corpus, on which it approaches state of the art performance using 10-fold cross validation, shows little decrease in performance with 2-folds, and achieves above 50% accuracy with as few as 10 examples.

Applying metric space and pivot-based indexing on combined features of bio-images for fast execution of composite queries

Content based recovery of bio images requires index structures, which can retrieve the similar image objects in time proficient way. Conventional Structure/ sequence based recovery of bio-images (for example, protein structures) experiences, tedious online similarity check from huge web based databases. The general approach of image feature representations follows vector based portrayal. In present manuscript, visual highlights of 3D protein structures and their content highlights have been implemented in isolated metric space, rather than vector space which advances the similarity recovery. At long last, the Visual highlights and Content based highlights are consolidated in one metric space, through the component results of highlight and substance metric. Results have demonstrated that pivot based ordering/ indexing on Combined Index Metric can undoubtedly execute composite content construct queries with respect to bio images in time effective way.

Hymenoptera Genome Database: Using HymenopteraMine to Enhance Genomic Studies of Hymenopteran Insects.

The Hymenoptera Genome Database (HGD; http://hymenopteragenome.org ) is a genome informatics resource for insects of the order Hymenoptera, which includes bees, ants and wasps. HGD provides genome browsers with manual annotation tools (JBrowse/Apollo), BLAST, bulk data download, and a data mining warehouse (HymenopteraMine). This chapter focuses on the use of HymenopteraMine to create annotation data sets that can be exported for use in downstream analyses. HymenopteraMine leverages the InterMine platform to combine genome assemblies and official gene sets with data from OrthoDB, RefSeq, FlyBase, Gene Ontology, UniProt, InterPro, KEGG, Reactome, dbSNP, PubMed, and BioGrid, as well as precomputed gene expression information based on publicly available RNAseq. Built-in template queries provide starting points for data exploration, while the QueryBuilder tool supports construction of complex custom queries. The List Analysis and Genomic Regions search tools execute queries based on uploaded lists of identifiers and genome coordinates, respectively. HymenopteraMine facilitates cross-species data mining based on orthology and supports meta-analyses by tracking identifiers across gene sets and genome assemblies.

Framing Big Data: The discursive construction of a radio cell query in Germany

The article examines the construction of “Big Data” in media discourse. Rather than asking what Big Data really is or is not, it deals with the discursive work that goes into making Big Data a soci...

Designing a Query Language for RDF

When querying an Resource Description Framework (RDF) graph, a prominent feature is the possibility of extending the answer to a query with optional information. However, the definition of this feature in SPARQL—the standard RDF query language—has raised some important issues. Most notably, the use of this feature increases the complexity of the evaluation problem, and its closed-world semantics is in conflict with the underlying open-world semantics of RDF. Many approaches for fixing such problems have been proposed, the most prominent being the introduction of the semantic notion of weakly monotone SPARQL query. Weakly monotone SPARQL queries have shaped the class of queries that conform to the open-world semantics of RDF. Unfortunately, finding an effective way of restricting SPARQL to the fragment of weakly monotone queries has proven to be an elusive problem. In practice, the most widely adopted fragment for writing SPARQL queries is based on the syntactic notion of well designedness. This notion has proven to be a good approach for writing SPARQL queries, but its expressive power has yet to be fully understood. The starting point of this article is to understand the relation between well-designed queries and the semantic notion of weak monotonicity. It is known that every well-designed SPARQL query is weakly monotone; as our first contribution we prove that the converse does not hold, even if an extension of this notion based on the use of disjunction is considered. Given this negative result, we embark on the task of defining syntactic fragments that are weakly monotone and have higher expressive power than the fragment of well-designed queries. To this end, we move to a more general scenario where infinite RDF graphs are also allowed, so interpolation techniques studied for first-order logic can be applied. With the use of these techniques, we are able to define a new operator for SPARQL that gives rise to a query language with the desired properties (over finite and infinite RDF graphs). It should be noticed that every query in this fragment is weakly monotone if we restrict the semantics to finite RDF graphs. Moreover, we use this result to provide a simple characterization of the class of monotone CONSTRUCT queries, that is, the class of SPARQL queries that produce RDF graphs as output. Finally, we pinpoint the complexity of the evaluation problem for the query languages identified in the article.

START: a system for flexible analysis of hundreds of genomic signal tracks in few lines of SQL-like queries

BackgroundA genomic signal track is a set of genomic intervals associated with values of various types, such as measurements from high-throughput experiments. Analysis of signal tracks requires complex computational methods, which often make the analysts focus too much on the detailed computational steps rather than on their biological questions.ResultsHere we propose Signal Track Query Language (STQL) for simple analysis of signal tracks. It is a Structured Query Language (SQL)-like declarative language, which means one only specifies what computations need to be done but not how these computations are to be carried out. STQL provides a rich set of constructs for manipulating genomic intervals and their values. To run STQL queries, we have developed the Signal Track Analytical Research Tool (START, http://yiplab.cse.cuhk.edu.hk/start/), a system that includes a Web-based user interface and a back-end execution system. The user interface helps users select data from our database of around 10,000 commonly-used public signal tracks, manage their own tracks, and construct, store and share STQL queries. The back-end system automatically translates STQL queries into optimized low-level programs and runs them on a computer cluster in parallel. We use STQL to perform 14 representative analytical tasks. By repeating these analyses using bedtools, Galaxy and custom Python scripts, we show that the STQL solution is usually the simplest, and the parallel execution achieves significant speed-up with large data files. Finally, we describe how a biologist with minimal formal training in computer programming self-learned STQL to analyze DNA methylation data we produced from 60 pairs of hepatocellular carcinoma (HCC) samples.ConclusionsOverall, STQL and START provide a generic way for analyzing a large number of genomic signal tracks in parallel easily.

VISUAL: Simulation of Visual Subgraph Query Formulation to Enable Automated Performance Benchmarking

Visual graph interfaces improve the usability of graph databases by making it easier for users to formulate queries. Recently, a variety of interactive query formulation-based techniques (e.g., blending of visual query construction and processing, visual query suggestions) have been proposed to enhance query performance and usability. Comprehensive user studies are needed to exhaustively and systematically evaluate performance of the proposed techniques, but, unfortunately, user studies are expensive and time consuming. To reduce the cost and time needed, we present a novel synthetic visual subgraph query simulator called VISUAL. VISUAL realistically simulates subgraph query construction without requiring human users. It can automatically generate test subgraph queries having different user-specified characteristics by utilizing the underlying indexes and simulate their formulation based on different query formulation sequences. A key feature of this simulator is that it is built on top of an HCI-inspired, extensible quantitative model which enables us to model the visual query formulation process quantitatively. Our experimental study demonstrates the effectiveness of VISUAL in accurately simulating visual subgraph queries.

AFLUX: The LUX materials search API for the AFLOW data repositories

Automated computational materials science frameworks rapidly generate large quantities of materials data for accelerated materials design. In order to take advantage of these large databases, users should have the ability to efficiently search and extract the desired data. Therefore, we have extended the data-oriented AFLOW-repository Application-Program-Interface (API) (Comput. Mater. Sci. 93, 178 (2014)) to enable programmatic access to search queries. A Uniform Resource Identifier (URI)-based search API is proposed for the construction of complex queries for remote creation and retrieval of customized data sets. It is expected that the new language, AFLUX, from “Automatic Flow of LUX (light)”, will enable remote search operations on the AFLOW set of computational materials science data repositories. In addition, AFLUX facilitates the verification and validation of the data in the AFLOW repositories.

A semantic-syntax model for XML query construction

PurposeWhen accessing structured contents in XML form, information requests are formulated in the form of special query languages such as NEXI, Xquery, etc. However, it is not easy for end users to compose such information requests using these special queries because of their complexities. Hence, the purpose of this paper is to automate the construction of such queries from common query like keywords or form-based queries.Design/methodology/approachIn this paper, the authors address the problem of constructing queries for XML retrieval by proposing a semantic-syntax query model that can be used to construct different types of structured queries. First, a generic query structure known as semantic query structure is designed to store query contents given by user. Then, generation of a target language is carried out by mapping the contents in semantic query structure to query syntax templates stored in knowledge base.FindingsEvaluations were carried out based on how well information needs are captured and transformed into a target query language. In summary, the proposed model is able to express information needs specified using query like NEXI. Xquery records a lower percentage because of its language complexity. The authors also achieve satisfactory query construction rate with an example-based method, i.e. 86 per cent (for NEXI IMDB topics) and 87 per cent (NEXI Wiki topics), respectively, compare to benchmark of 78 per cent by Sumita and Iida in language translation.Originality/valueThe proposed semantic-syntax query model allows flexibility of accommodating new query language by separating the semantic of query from its syntax.

Ontology Based Data Access in Statoil

Ontology Based Data Access (OBDA) is a prominent approach to query databases which uses an ontology to expose data in a conceptually clear manner by abstracting away from the technical schema-level details of the underlying data. The ontology is ‘connected’ to the data via mappings that allow to automatically translate queries posed over the ontology into data-level queries that can be executed by the underlying database management system. Despite a lot of attention from the research community, there are still few instances of real world industrial use of OBDA systems. In this work we present data access challenges in the data-intensive petroleum company Statoil and our experience in addressing these challenges with OBDA technology. In particular, we have developed a deployment module to create ontologies and mappings from relational databases in a semi-automatic fashion; a query processing module to perform and optimise the process of translating ontological queries into data queries and their execution over either a single DB of federated DBs; and a query formulation module to support query construction for engineers with a limited IT background. Our modules have been integrated in one OBDA system, deployed at Statoil, integrated with Statoil’s infrastructure, and evaluated with Statoil’s engineers and data.